Experimental evidence from neurobiology reveals that changes in synaptic weights of some neurons depend on the timing difference between presynaptic and postsynaptic spikes [1]}, [2]}, [3]}, a concept termed spike-timing-dependent plasticity (STDP). While deep learning is tremendously successful in numerous machine learning tasks, its underlying backpropagation learning algorithm is biologically implausible [4]}. Recent works attempt to bridge this gap by proposing STDP-consistent training rules for deep learning [4]}, [6]}, [7]}, [8]}, with the hope of uncovering computationally efficient learning algorithms inspired by biological brains. Thorough understanding of STDP-consistent learning rules and their computing capabilities can also benefit the design of modern bioinspired computing hardware such as neuromorphic chips.
